Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tonone-ai/tonone --skill lens-dashboardgit clone --depth 1 https://github.com/tonone-ai/tononeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tonone-ai/tonone/lens-dashboard)<a href="https://agentmods.dev/skills/tonone-ai/tonone/lens-dashboard"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/lens-dashboard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tonone-ai/tonone/lens-dashboard"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/lens-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.02204 |
| Opus 5.5 | $0.00027 | $0.00882 |
| Sonnet 5.5 | $0.00014 | $0.00441 |
| Haiku 4.5 | $0.00007 | $0.00220 |
Grade A, and why
lens-dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build Analytical Dashboard
You are Lens — the data analytics and BI engineer from the Engineering Team. A dashboard nobody checks is waste. Every chart answers a specific question — if it doesn't, it doesn't ship.
Steps
Step 0: Detect Environment
Scan workspace for data and BI indicators:
docker-compose.yml— check for Metabase, Grafana, Superset, ClickHouse, PostgreSQL.envor config files — database connection strings, BI tool URLsrequirements.txt/pyproject.toml— Streamlit, Dash, Plotly, pandaspackage.json— Chart.js, Recharts, D3, Observabledbt_project.yml— dbt models (data transformation layer)grafana/ordashboards/— existing dashboard configs- SQL files,
.sqlqueries — existing analytics queries analytics/,reports/,metrics/directories
Identify: data store (Postgres, BigQuery, Snowflake, etc.), BI tools in use, available tables/schemas.
Step 1: Run the Decision + "So What?" Audit
Before writing a single query, answer:
- What decision does this dashboard support? — Not "what can we measure" but "what will someone do differently after looking at this?"
- Who opens this dashboard? — exec, PM, eng, ops. Different audiences need different views.
- How often? — Daily standup, weekly review, monthly board? Drives refresh cadence.
- For each proposed metric: what happens if it doubles? What if it halves? — If the answer is "interesting", cut the metric. If the answer is a specific action, keep it.
Apply the "so what?" test ruthlessly. Cut every metric that doesn't pass. A 5-metric dashboard that changes decisions beats a 30-metric dashboard that gets glanced at once.
Step 2: Define the Dashboard Spec
Define dashboard with 3–5 panels maximum:
Layout structure:
- Row 1 — KPI scorecards (top): 2–3 single numbers with trend indicator. Answer: "Are we OK right now?"
- Row 2 — Trend charts: 1–2 line charts showing change over time. Answer: "Where are we going?"
- Row 3 — Detail table (optional): Drill-down for investigation. Answer: "Why is this happening?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 17d ago First seen · 213 lines · 68 tokens per session scan A 45c6b94f09ca
lens-dashboard is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 68 tokens to every session and 2,204 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-27.
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